The explosion of e-commerce and ride-hailing / food-delivery applications have fueled the need for a more accurate and reliable estimation of delivery times. The current common estimation of delivery time is based on Estimated Time of Arrival (ETA) which relies on route distance that is calculated between the origin and the desired destination. It only considers the duration from the pick up to drop off and does not consider the additional time needed for preparing the goods and offloading the goods. RP has developed a Machine Learning (ML) model that is able to calculate the stop duration which together with ETA provides the Estimated Time of Completion (ETC).
ML model enables prediction on ETC using a small set of input parameters such as building name / block number, road, postal codes, and time.
Model can be integrated with existing web / mobile based solutions.
Customer Services / Call Centres
Fleet Management
Loading Bay Assignment
Low cost and easy to implement.
Simple to use as only small set of input parameters are required.
Predict the time taken at each delivery stop with reasonable accuracy.
This model can be used to improve the existing route planning systems as it provides additional job completion prediction on top of ETA.
This technology is available for industry partners for collaborations and licensing.
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